In general, in case a frame of image is resolved into M.times.N picture cells, to each of which a quantitizing code of m bits is assigned, the maximum information content of the image becomes M.times.N.times.m bits. Supposed that m is 6 bits (2.sup.6 =64 levels), a frame of image is supposed to have the information content of 256 dots.times.256 dots.times.6 bits (=393216 bits).
Meanwhile, many images actually treated have offset gradation values rather than those of equal probability. Thus, it will be noted that the information content required for expressing a frame of image will be considerably less than M.times.N.times.m bits. The difference between the maximum information content and the actual information content is called redundancy and the deletion of the redundancy corresponds to the compression of the image data.
From the aforementioned view point, there have been conventionally proposed various methods of compressing the image data, all of which have a fundamental idea of Run-Length with which Huffman coding method is combined.
Such a conventional method of compressing image data will be described hereinjustbelow.
A series of identical signals on a scanning line are generally called Run and the length of Run is called Run-Length. Supposed that an occurence probability of a certain Run-Length is Q (i) and that the following expression is established for each Q(i); EQU Q(1).gtoreq.Q(2).gtoreq.Q(3).gtoreq. - - - .gtoreq.Q(n)
At that time, each Q (i) has a given code and a code length L (i) is determined as follows; EQU L(1).ltoreq.L(2).ltoreq.L(3).ltoreq. - - - .ltoreq.L(n)
It will be noted that such a coding process can shorten the codes corresponding to Run-Lengths as they occur with higher probability.
As an example, supposed that the occurrence probabilities Q (i) of the Run-Lengths a1 through a4 on TABLE I are 0.4, 0.3, 0.2, 0.1, respectively, a coding process can be accomplished as shown on TABLE II. Such a coding process is called Huffman shortest coding method.
TABLE I ______________________________________ ai a1 a2 a3 a4 ______________________________________ Q(i) 0.4 0.3 0.2 0.1 ______________________________________
TABLE II ______________________________________ aiIIIIII Code L (i) ______________________________________ ##STR1## 1 01000001 1233 ______________________________________
As noted from TABLE II, a code of 1 bit is assigned to the Run-Length a1 of highest occurrence probability while codes of 3 bits are assigned to the Run-Lengths a3 and a4 of lower occurrence probability and it will be noted from this that the information contents are compressed as a whole.
The aforementioned information compression method has been generally used for compressing such image data as two value facsimile signals having simple white and black colors because it effectively functions for them. However, it cannot function effectively for compression of such image data for picture cells of multilevel gradation values. For example, in case television images are transmitted while they are digitized, gradation informations of m bits are applied to the digitized information. The Run-Length is never elongated as in the two value informations (which corresponds to m=1) due to variation in the gradation information. Thus, it will be noted that the conventional method has no effect of compression of multilevel gradation image data.